An Overview of Principal Component Analysis

Sasan Karamizadeh, Shahidan M. Abdullah, Azizah A. Manaf, Mazdak Zamani, Alireza Hooman
2013 Journal of Signal and Information Processing  
The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section
more » ... the PCA section very straightforward, but can be skipped if the concepts are already familiar.
doi:10.4236/jsip.2013.43b031 fatcat:2ub52fie2bcj5de37oqyq7xzte